A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
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README.rst

Feature Forge

This library provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, etc.), and particularly helpful if you use scikit-learn (although this can work if you have a different algorithm).

Most machine learning problems involve an step of feature definition and preprocessing. Feature Forge helps you with:

  • Defining and documenting features
  • Testing your features against specified cases and against randomly generated cases (stress-testing). This helps you making your application more robust against invalid/misformatted input data. This also helps you checking that low-relevance results when doing feature analysis is actually because the feature is bad, and not because there's a slight bug in your feature code.
  • Evaluating your features on a data set, producing a feature evaluation matrix. The evaluator has a robust mode that allows you some tolerance both for invalid data and buggy features.

Installation

Just pip install featureforge.

Documentation

Documentation is available at http://feature-forge.readthedocs.org/en/latest/

Contact information

Feature Forge is © 2014 Machinalis (http://www.machinalis.com/). Its primary authors are:

Any contributions or suggestions are welcome, the official channel for this is submitting github pull requests or issues.

Changelog

0.1: Initial release